A background elimination method based on linear programming for Raman spectra

Abstract

In this paper, we consider a new background elimination method for Raman spectra. As a background is usually slowly varying with respect to wavelength, it could be approximated by a slowly varying curve. However, the usual curve-fitting method cannot be applied because there is a constraint that the estimated background must be beneath a measured spectrum. To meet the requirement, we adopt a polynomial as an approximating function and show that background estimation could be converted to a linear programming problem which is a special case of constrained optimization. In addition, we present an order selection algorithm for automatic baseline elimination. According to the experimental results, it is shown that the proposed method could be successfully applied to experimental Raman spectra as well as synthetic spectra.

MoO3 incorporation in magnesium aluminosilicate glasses

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“…Many others have proposed various theories to pre-process the Raman spectra to get rid of the substrate background contribution. [16,[19][20][21][22][23][24] New trend in recent decade is towards lab-on-a-chip applications for live cell Raman imaging. [25,26] Lab-on-a-chip requires fabrication of substrates to achieve enclosed fluidic compartment.…”

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